The present invention relates to image quality enhancement, and more particularly, to an image quality enhancing circuit having functions such as noise reduction, contrast enhancement based on histogram equalization, local contrast enhancement and color compensation, and a method therefor.
In general, the image quality of a video signal can be deteriorated due to various factors. Low contrast is a factor in video signal and image quality degradation, but is only one among several factors. Gamma correction is a method for correcting image quality degradation involving correction according to a variation in brightness, histogram equalization, etc.
The principal operation of the histogram equalization is to convert a given input image on the basis of the histogram of the input image. Here, the histogram represents gray level distribution at a given input image. Such a gray level histogram provides an entire depiction on the appearance of an image. A gray level appropriately adjusted according to the sample distribution of an image enhances the appearance and contrast of the image.
The histogram equalization for enhancing the contrast of a given image according to sample distribution of the image is the most widely known among various contrast enhancing methods, and is fully discussed in the following documents: [1] J. S. Lim, "Two-Dimensional Signal and Image Processing," Prentice Hall, Englewood Cliffs, N.J., 1990; and [2] R. C. Gonzalez and P. Wints, "Digital Image Processing," Addison-Wesley, Reading, Mass., 1977, both of which are hereby incorporated in their entirety for reference as useful background material.
Also, useful applications of the histogram equalization including medical image processing and radar image processing are discussed in the following documents, each of which also is incorporated in its entirety for useful background: [3] J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney, and B. Brenton, "Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement," IEEE Tr.on Medical Imaging, pp. 304-312, Dec. 1998; and [4] Y. Li, W. Wang, and D. Y. Yu, "Application of adaptive histogram equalization to x-ray chest image," Proc. of the SPIE, pp. 513-514, vol. 2321,1994. Accordingly, a technique using the histogram of a given image has been usefully applied to various fields such as medical image processing, infrared ray image processing, and radar image processing.
In general, since the histogram equalization has an effect of stretching a dynamic range, it flattens the distribution density of a resultant image. Therefore, the contrast of the image is enhanced. Such a well-known characteristic of the histogram equalization, however, becomes a defect in some actual cases. That is, because histogram equalization flattens the image output density, the average brightness of the output image approaches a middle gray level. In practice, for histogram equalization of an analog image, the average brightness of an output image in the histogram equalization is exactly the middle gray level regardless of the average brightness of an input image. Obviously, the above-described characteristic is not desirable for the practical application. As an example of this problem, a scene photographed at night looks too bright after the histogram equalization is generated.
Impulse noise is another factor of image quality degradation. The independent impulse noise is uniformly distributed on a frequency region. As a result, an application of a simple linear filter causes details of an image to blur, and thus a high frequency component of the impulse noise is not effectively removed.
Still another factor causing image quality degradation is that the gamma correction or the histogram equalization for enhancing low contrast enhances an entire contrast of a video signal, but is not very effective for the enhancement of a contrast at detailed portions being visually more important information, i.e., a local contrast.
Yet another factor causing image quality degradation is color compensation. Unless color compensation is performed on a color signal according to a variation in luminance (occurring when a predetermined luminance processing such as the histogram equalization is performed on a luminance signal) to enhance contrast, a primary color signal is distorted.